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Filtering by: Creator Buehrens, Thomas W. Remove constraint Creator: Buehrens, Thomas W. Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science

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  • A probabilistic model for anomaly detection in remote sensor streams

    Creator:
    Dereszynski, Ethan W.
    Abstract:
    Remote sensors are becoming the standard for observing and recording ecological data in the field. Such sensors can record data at fine temporal resolutions, and they can operate under extreme conditions prohibitive to human access. Unfortunately, sensor data streams exhibit many kinds of errors ranging from corrupt communications to partial...
    Resource Type:
    Masters Thesis
    Full Text:
    /thesis.dvi AN ABSTRACT OF THE THESIS OF Ethan W. Dereszynski for the degree of Masters of Science in

  • Probabilistic models for quality control in environmental sensor networks

    Creator:
    Dereszynski, Ethan W.
    Abstract:
    Networks of distributed, remote sensors are providing ecological scientists with a view of our environment that is unprecedented in detail. However, these networks are subject to harsh conditions, which lead to malfunctions in individual sensors and failures in network communications. This behavior manifests as corrupt or missing measurements in the...
    Resource Type:
    Dissertation
    Full Text:
    Environmental Monitoring Campaigns Ethan W. Dereszynski and Thomas G. Dietterich ACM Transactions on Sensor

  • Fabrication and characterization of p-type CuO / n-type ZnO heterostructure gas sensors prepared by sol-gel processing techniques

    Creator:
    Ravichandran, Ram
    Abstract:
    Increased interest in the field of sensor technology stems from the availability of an inexpensive and robust sensor to detect and quantify the presence of a specific gas. Bulk p-CuO/n-ZnO heterocontact based gas sensors have been shown to exhibit the necessary sensitivity and selectivity characteristics, however, low interfacial CuO/ZnO contact...
    Resource Type:
    Masters Thesis
    Full Text:
    Abstract approved: Thomas K. Plant Increased interest in the �eld of sensor technology stems from the

  • Design and analysis techniques for nano-joule ADCs and sampling linearity

    Creator:
    Brown, Thomas (Thomas William)
    Abstract:
    Two aspects of ADC system performance are addressed in this work. First, the combination of the ADC and its associated reference are co-designed for an energy constrained remote sensing system. Second, sampling linearity is mathematically analyzed as a function of frequency to provide enhanced understanding into an ADC's requisite sampling...
    Resource Type:
    Dissertation
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF Thomas Brown for the

  • Efficient Algorithms for Robust Spatiotemporal Data Analysis

    Creator:
    Moore, Travis W.
    Abstract:
    Many large-scale data analysis applications involve data that can vary over both time and space. Often the primary goal of analyzing spatiotemporal data is identifying trends, movements, and sudden changes with respect to time, location, or both. This can include a variety of applications in economics (housing prices, unemployment, job...
    Resource Type:
    Dissertation
    Full Text:
    update as Zt+1 = QtRt + v′Xi = Q t(Rt +w′Xi) (2.12) where w′ = (Qt)−1v′ = (Qt)′v′. Our goal is to turn

  • CAM : a new Circuit Augmentation Method for modeling interconnects and passive components

    Creator:
    Kolstad, Joel W.
    Abstract:
    Contemporary circuit design involves significant computer-based simulation, calling for a balance between circuit model accuracy and simulation run time. Traditionally, circuit modelers concentrated on producing either (1) physically-based models, where each element in the model correlates to a physically meaningful aspect of the device being modeled or (2) mathematically-based "macromodels,"...
    Resource Type:
    Masters Thesis
    Full Text:
    AN ABSTRACT OF THE THESIS OF Joel W. Kolstad for the degree of

  • Sequential supervised learning and conditional random fields

    Creator:
    Ashenfelter, Adam J.
    Abstract:
    Supervised learning is concerned with discovering the relationship between example sets of features and their corresponding classes. The traditional supervised learning formulation assumes that all examples are independent from one another. The order of the examples contains no information. Nonetheless, many problems have a sequential nature. Classifiers for these problems...
    Resource Type:
    Masters Thesis
    Full Text:
    Supervised Learning and Conditional Random Fields. Abstract approved: Thomas G. Dietterich Supervised

  • Learning MDP action models via discrete mixture trees

    Creator:
    Wynkoop, Michael S.
    Abstract:
    This thesis addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree models of the conditional probability distributions of the DBNs. Existing algorithms presume that the stochasticity in the domain can be modeled as a deterministic function with additive noise....
    Resource Type:
    Masters Thesis
    Full Text:
    , 2008. Title: Learning MDP Action Models via Discrete Mixture Trees Abstract approved: Thomas G

  • Evaluating online text classification algorithms for email prediction in TaskTracer

    Creator:
    Keiser, Victoria L.
    Abstract:
    This paper examines how six online multiclass text classification algorithms perform in the domain of email tagging within the TaskTracer system. TaskTracer is a project-oriented user interface for the desktop knowledge worker. TaskTracer attempts to tag all documents, web pages, and email messages with the projects to which they are...
    Resource Type:
    Masters Thesis
    Full Text:
    TaskTracer Abstract approved: Thomas G. Dietterich This paper examines how six online

  • Machine Learning Methods for Computational Sustainability

    Creator:
    Liu, Liping
    Abstract:
    Maintaining the sustainability of the earth’s ecosystems has attracted much attention as these ecosystems are facing more and more pressure from human activities. Machine learning can play an important role in promoting sustainability as a large amount of data is being collected from ecosystems. There are at least three important...
    Resource Type:
    Dissertation
    Full Text:
    Methods for Computational Sustainability Abstract approved: Thomas G. Dietterich Maintaining the